An index of algorithms for learning causality with data.
Please cite our survey paper if this index is helpful.
@article{guo2018survey,
title={A Survey of Learning Causality with Data: Problems and Methods},
author={Guo, Ruocheng and Cheng, Lu and Li, Jundong and Hahn, P. Richard and Liu, Huan},
journal={arXiv preprint arXiv:1809.09337},
year={2018}
}
Updates on Counterfactual Fairness/Explanantions 01/30/2020, PRs are welcome!
Name | Paper | Code |
---|---|---|
DoWhy | Amit Sharma and Emre Kiciman. "Tutorial on Causal Inference and Counterfactual Reasoning." In ACM SIGKDD 2018 | Python |
TETRAD toolbox | Ramsey, Joseph D., Kun Zhang, Madelyn Glymour, Ruben Sanchez Romero, Biwei Huang, Imme Ebert-Uphoff, Savini Samarasinghe, Elizabeth A. Barnes, and Clark Glymour. "TETRAD-AToolbox FOR CAUSAL DISCOVERY." | R |
CausalDiscoveryToolbox | Kalainathan, Diviyan, and Olivier Goudet. "Causal Discovery Toolbox: Uncover causal relationships in Python." arXiv preprint arXiv:1903.02278 (2019). | Python |
Uber CausalML | NA | Python |
JustCause | For evaluation of heterogeneous treatment effect estimators on common reference as well as synthetic data. Underlying thesis | Python |
Name | Paper | Code |
---|---|---|
RespSVM | Kallus, Nathan. "Classifying Treatment Responders Under Causal Effect Monotonicity." arXiv preprint arXiv:1902.05482 (2019) | NA |
Name | Paper | Code |
---|---|---|
Variable importance through targeted causal inference | The Github Repo "varimpact" by Alan E. Hubbard and Chris J. Kennedy, University of California, Berkeley | R |
Name | Paper | Code |
---|---|---|
Network Deconfounder | Guo, Ruocheng, Jundong Li, and Huan Liu. "Learning Individual Causal Effects from Networked Observational Data." WSDM 2020 (to appear). | Python |
Causal Inference with Network Embeddings | Veitch, Victor, Yixin Wang, and David M. Blei. "Using embeddings to correct for unobserved confounding." arXiv preprint arXiv:1902.04114 (2019). | Python |
Name | Paper | Code |
---|---|---|
Vansteelandt, Stijn, and Marshall Joffe. "Structural nested models and G-estimation: the partially realized promise." Statistical Science 29, no. 4 (2014): 707-731. | NA | |
Counterfactual-Guided Policy Search (CF-GPS) | Buesing, Lars, Theophane Weber, Yori Zwols, Sebastien Racaniere, Arthur Guez, Jean-Baptiste Lespiau, and Nicolas Heess. "Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search." arXiv preprint arXiv:1811.06272 (2018). | NA |
Name | Paper | Code |
---|---|---|
Offline+MAB | Ye, Li, Yishi Lin, Hong Xie, and John Lui. "Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decisions." arXiv preprint arXiv:2001.05699 (2020). | NA |
Incremental Model | Sawant, Neela, Chitti Babu Namballa, Narayanan Sadagopan, and Houssam Nassif. "Contextual Multi-Armed Bandits for Causal Marketing." arXiv preprint arXiv:1810.01859 (2018). | NA |
Casual Bandit | Lee, Sanghack, and Elias Bareinboim. Structural Causal Bandits with Non-manipulable Variables. Technical Report R-40, Purdue AI Lab, Department of Computer Science, Purdue University, 2019. | NA |
Name | Paper | Code |
---|---|---|
Deep Global Balancing Regression | Kuang, Kun, Peng Cui, Susan Athey, Ruoxuan Xiong, and Bo Li. "Stable Prediction across Unknown Environments." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1617-1626. ACM, 2018. | NA |
A Simple Algorithm for Invariant Prediction | Julia |
Name | Paper | Code |
---|---|---|
Odena, Augustus, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, and Ian Goodfellow. "Is Generator Conditioning Causally Related to GAN Performance?." arXiv preprint arXiv:1802.08768 (2018). | NA | |
Causal GAN | Kocaoglu, Murat, Christopher Snyder, Alexandros G. Dimakis, and Sriram Vishwanath. "CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training." arXiv preprint arXiv:1709.02023 (2017). | Python |
Name | Paper | Code |
---|---|---|
IC algorithm | Python | |
PC algorithm | P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, 2nd edition, 2000. | Python R Julia |
FCI algorithm | R |
Name | Paper | Code |
---|---|---|
BMLiNGAM | S. Shimizu and K. Bollen. Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. Journal of Machine Learning Research, 15: 2629-2652, 2014. | Python |
Name | Paper | Code |
---|---|---|
RCIT | R |
Name | Paper | Code |
---|---|---|
Causal PSL | Sridhar, Dhanya, Jay Pujara, and Lise Getoor. "Scalable Probabilistic Causal Structure Discovery." In IJCAI, pp. 5112-5118. 2018. | Java |